With the advent of autonomous driving and augmented reality, the applications of visual odometry are significantly growing. The development of smart-phones and cameras is also making the visual odometry more accessible to common users in daily life. With the increasing efforts devoted to accurately computing the position information, emerging applications based on location contexts, such as scene understanding, city navigation, and tourist recommendation, have gained significant growth. The location information can bring a rich context to facilitate a large number of challenging problems, such as landmark and traffic sign recognition under various weather and light conditions, and computer vision applications on entertainment based on location information, such as Pokemon. The motivation for the proposed workshop is soliciting scalable algorithms and systems for addressing the ever-increasing demand for accurate and real-time visual odometry, as well as the methods and applications based on the location clues. This workshop invites papers in the areas including advances in visual odometry and its applications related to computer vision in topics listed below, but not limited:


Image-based localization and navigation
Monocular and stereo visual odometry
Visual odometry applications on autonomous driving
Augmented reality based on visual odometry
Robust pose estimation solutions
Multi-model visual sensor data fusion
Real-time object tracking
3D scene modeling
Application of deep learning on visual odometry
Large-scale SLAM
Map generation
Scene understanding and semantic labeling
Rendering and visualization of large-scale models
Feature representation, indexing, storage and analysis
Feature extraction and matching
Object detection and recognition based on location context
Landmark mining and tourism recommendation
Camera calibration
Video surveillance
Benchmark datasets collection


Papers accepted by our workshop will be published at IEEE Xplore, CVPR proceedings and CVF website.


Organizers/Program chairs:
Guoyu Lu, Rochester Institute of Technology
Friedrich Fraundorfer, Graz University of Technology
Yan Yan, Texas State University
Nicu Sebe, University of Trento
Chandra Kambhamettu, University of Delaware